/s1.cluster_plot.jpg') plt.close() 细胞注释后的标签图: 2.运行配体受体分析 运行:st.tl.cci.run这一步耗时比较久,建议在集群上进行分析,n_pairs.../s2.lr_summary500.jpg') plt.close() st.pl.lr_summary(data, n_top=50, figsize=(10,3)) plt.savefig('..../s5.lr_plot-1.jpg') plt.close() 红色为配体,绿色为受体,蓝色为共表达。有助于了解配体/受体在哪里以及在多大程度上表达。.../s5.lr_plot-5.jpg') plt.close() 外点显示配体(红色)、受体(绿色)和共表达(蓝色)的表达。.../s12.lr_plot_{best_lr}.jpg') plt.close() # 保存数据 # 设置备份地址 data.filename = '.
', fontdict={'size':14, 'color':'red'}) # 显示图像 plt.show() plt.close() """ 画图种类 """ # 散点图 SIZE = 1024...np.random.normal(0, 1, SIZE) T = np.arctan2(y, x) plt.scatter(x, y, s=75, c=T, alpha=.5) plt.show() plt.close...none', cmap='bone', origin='lower') plt.colorbar(shrink=.1) plt.xticks(()) plt.yticks(()) plt.show() plt.close...plt.subplot(2,2,3) plt.plot([0, 1], [0,10]) plt.subplot(2,2,4) plt.plot([0, 1], [0,15]) plt.show() plt.close...plt.subplot(2,3,5) plt.plot([0, 1], [0,10]) plt.subplot(2,3,6) plt.plot([0, 1], [0,15]) plt.show() plt.close
plt.close('all') fig = plt.figure() ax1 = plt.subplot(221) ax2 = plt.subplot(223) ax3 = plt.subplot(...plt.close('all') fig = plt.figure() ax1 = plt.subplot2grid((3, 3), (0, 0)) ax2 = plt.subplot2grid((3...arr = np.arange(100).reshape((10,10)) plt.close('all') fig = plt.figure(figsize=(5,4)) ax = plt.subplot...plt.close('all') fig = plt.figure() import matplotlib.gridspec as gridspec gs1 = gridspec.GridSpec(...plt.close('all') arr = np.arange(100).reshape((10,10)) fig = plt.figure(figsize=(4, 4)) im = plt.imshow
plt.tight_layout() 当绘制多个子图时,每个图的 ticklabels 可能会和其它图出现重叠 plt.close('all') fig, ((ax1, ax2), (ax3, ax4...plt.close('all') fig = plt.figure() ax1 = plt.subplot2grid((3, 3), (0, 0)) ax2 = plt.subplot2grid((3...plt.close('all') fig = plt.figure() import matplotlib.gridspec as gridspec gs1 = gridspec.GridSpec(...plt.close('all') fig = plt.figure() from mpl_toolkits.axes_grid1 import Grid grid = Grid(fig, rect=111...另一种方法时使用 AxesGrid1 为 colorbar 创建一个 axes plt.close('all') arr = np.arange(100).reshape((10,10)) fig =
person - 1) id_money_dict[id_get] += 1 se = pd.Series(id_money_dict) se.plot.bar() plt.show() plt.close...() plt.show() plt.close() se.plot.hist(bins=100) plt.show() 结果的图如下: ?
n\n\n\n\n\n\n\n\n\n\n' plt.text(0, 0, t, ha='left', wrap=True, fontproperties=myfont) pdf.savefig() plt.close...plt.axis('off') plt.text(0, 0, word_T1, ha='left', wrap=True, fontproperties=myfont) pdf.savefig() plt.close...plt.axis('off') plt.text(0, 0, word_T2, ha='left', wrap=True, fontproperties=myfont) pdf.savefig() plt.close...height, str(height), ha='center', va='bottom') plt.savefig('Top10 words.jpg') pdf.savefig() # plt.show() plt.close...plt.title('聊天时间分布', fontproperties=myfont) plt.savefig('time frequency.jpg') pdf.savefig() # plt.show() plt.close
-0.4,'%d'%int(a),ha='center',va='bottom') plt.yticks(x,Yi) # plt.legend() plt.show() plt.close...%d' % int(a), ha='center', va='bottom') plt.yticks(x, Yi) # plt.legend() plt.show() plt.close...my_wordcloud = wc.generate(text) plt.imshow(my_wordcloud) plt.axis("off") plt.show() plt.close...%d' % int(a), ha='center', va='bottom') plt.yticks(x, Yi) # plt.legend() plt.show() plt.close...-0.4,'%d'%int(a),ha='center',va='bottom') plt.yticks(x,Yi) # plt.legend() plt.show() plt.close
# 保存图片文件 filename = f'{num}.png' filenames.append(filename) plt.savefig(filename) plt.close...for i in range(15): filenames.append(filename) # 保存 plt.savefig(filename) plt.close...5): filenames.append(filename) # 保存图片 plt.savefig(filename) plt.close...filenames.append(filename) # 保存 plt.savefig(filename, dpi=96, facecolor=bg_color) plt.close...filename) # 保存 plt.savefig(filename, dpi=96, facecolor=bg_color) plt.close
np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8) # 设置numpy数组大小为图像大小 vis_img.shape = (h, w, 3) plt.close...数组大小为图像大小 vis_img.shape = (h, w, 3) # 将RGB格式转换为BGR格式 vis_img = cv2.cvtColor(vis_img, cv2.COLOR_RGB2BGR) plt.close
True) display(f) # close the figure at the end, so we don't get a duplicate # of the last plot plt.close
path): os.remove(path) fig.savefig(path, dpi=90, bbox_inches='tight') # print(0) plt.close...plt.show() if save_path is not None: fig.savefig(save_path, dpi=90, bbox_inches='tight') plt.close
#print("{}/{}".format(i+1,width*height)) fig.savefig(savename, dpi=100) fig.clf() plt.close...0]) plt.savefig("{}/f9_avgpool.png".format(savepath)) plt.clf() plt.close...[0, :]) plt.savefig("{}/f10_fc.png".format(savepath)) plt.clf() plt.close
#selected_vals = column used to plotdef boxplot(selected_vals): plt.close('all') fig = plt.figure...disabled=False)复制代码 散点图绘制# scatter plot functiondef scatter(x,y,category): plt.close
#selected_vals = column used to plot def boxplot(selected_vals): plt.close('all') fig = plt.figure...disabled=False) 散点图绘制 # scatter plot function def scatter(x,y,category): plt.close
text # Close any previously open graphics windows # ToDo: fails to work within Eclipse plt.close...main__': # Close any previously open graphics windows # ToDo: fails to work within Eclipse plt.close
set_ticks([]) ax0.get_yaxis().set_ticks([]) plt.show() plt.savefig("in_img1.png", bbox_inches="tight") plt.close...set_ticks([]) ax1[2, 1].set_title("L1-Map2ReLUPool") plt.show() plt.savefig("L1.png", bbox_inches="tight") plt.close...set_ticks([]) ax2[2, 2].set_title("L2-Map3ReLUPool") plt.show() plt.savefig("L2.png", bbox_inches="tight") plt.close...set_ticks([]) ax3[2].set_title("L3-Map1ReLUPool") plt.show() plt.savefig("L3.png", bbox_inches="tight") plt.close
fatal_ratio":fatal_ratio}) company = fatal_crash.groupby(['company']).apply(airplane_counte) print(company) plt.close...98 non-null float64 time 98 non-null float64 dtypes: float64(3) memory usage: 3.1+ KB plt.close...8388.0 0.778758 452.0 11 10033.0 0.766522 454.0 12 10459.0 0.668478 516.0 plt.close...["Fatalities"].sum() / x["Aboard"].sum()}) hour = time_fatal.groupby(["hour"]).apply(hour_analysis) plt.close
filename='Animated_Bubble_Chart/'+str(i)+'.png' plt.savefig(fname=filename, dpi=96) plt.gca() plt.close...animation.FuncAnimation( fig, animate, frames=len(years), interval=500, # 0.5秒 repeat=True) plt.close
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